15 papers
Atompack: A Storage and Distribution Layer for Read-Heavy Atomistic ML Training Datasets
Ali Ramlaoui, Daniel T. Speckhard, Sagar Pal +3
Atomistic machine learning datasets are increasingly used for training: large immutable snapshots are read repeatedly, shuffled across epochs, staged across clusters' storage syste…
Scaling Higher-Order Graph Learning with Maximal Clique Complexes
Antoine Vialle, Aref Einizade, Fragkiskos D. Malliaros +1
Graph neural networks (GNNs) are limited to modeling pairwise interactions, while higher-order models based on cell complexes achieve greater expressivity but often suffer from poo…
TriForces: Augmenting Atomistic GNNs for Transferable Representations
Ali Ramlaoui, Alexandre Duval, Hannah Bull +4
Machine learning interatomic potentials (MLIPs) achieve excellent accuracy when trained on large Density Functional Theory (DFT) data. To be useful in practice, they must often be…
Generalization Bounds for Spectral GNNs via Fourier Domain Analysis
Vahan A. Martirosyan, Daniele Malitesta, Hugues Talbot +2
Spectral graph neural networks learn graph filters, but their behavior with increasing depth and polynomial order is not well understood. We analyze these models in the graph Fouri…
Training-free Graph-based Imputation of Missing Modalities in Multimodal Recommendation
Daniele Malitesta, Emanuele Rossi, Claudio Pomo +2
Multimodal recommender systems (RSs) represent items in the catalog through multimodal data (e.g., product images and descriptions) that, in some cases, might be noisy or (even wor…
Continuous Simplicial Neural Networks
Aref Einizade, Dorina Thanou, Fragkiskos D. Malliaros +1
Simplicial complexes provide a powerful framework for modeling higher-order interactions in structured data, making them particularly suitable for applications such as trajectory p…